I will skip the usual “I’m thrilled and excited…”. I’ll just say it.
As of today, I am the CTO of wizer.me, a platform for teachers and educators to create and share interactive worksheets.

On a scale of 1 to 10, how thrilled am I? 10
On a scale of 1 to 10, how terrified am I? 10
On a scale of 1 to 10, how confident am I that wizer.me will become the “next big thing” and the most significant chapter in my career? You won’t believe me, but also 10.

Today, I gave my first in-person presentation since the pandemic. It was awesome! I was talking about the study I performed with Nabeel Sulieman about data visualization in environments that use right-to-left writing systems.

I wrote about this study in the past [one, two]. Today, you may find the results of our study at http://direction-matters.com/. I hope to be able to publish the video recording of this presentation really soon.

An example of a very bad graph

Nature Medicine is a peer-reviewed journal that belongs to the very prestigious Nature group. Today, I was reading a paper that included THIS GEM.

These two graphs are so bad. It looks as if the authors had a target to squeeze as many data visualization mistakes as possible in a single piece of graphics.

Let’s take a look at the problems.

• Double Y axes. Don’t! Double axes are bad in 99% of cases (exceptions do exist, but they are rare).
• Two subgraphs that are meant to work together have different category orders and different Y-axis scales. These differences make the comparison much harder.
• Inverted Y scale in a bar chart. Wow! This is very strange. Bizarre! It took me a while to spot this. First, I tried to understand why the line of P<0.05 (the magic value of statistics) is above 0.1. Then, I realized that the right Y-axis is reversed. At first, I thought, “WTF?!” but then I understood why the authors made this decision. You see, according to the widespread statistical ritual, the lower the “P-value” is, the more significant it is considered. The value of 1 is deemed to be non-significant at all, and the value of 0 is considered “as significant as one can have.” So, in theory, the authors could have renamed the axis to “Significance” and reversed the numbers. Still, the result would not be a real “significance,” nor would the name be intuitive to anyone familiar with statistical analysis. On the other hand, they really wanted more “significant” values to be bigger than less significant ones. So, what the heck? Let’s invert the scale! Well, no, this is not a good idea
• Slanted category labels. This might be a matter of taste, but I dislike rotated and slanted labels. Turning the graph solves the need for label rotation, thus making it more readable and having zero drawbacks.

What can be done?

I don’t like criticism without improvement suggestions. Let’s see what I would have done with this graph. To make this decision, I first need to decide what I want to show. According to my understanding of the paper, the authors wish to show that the two data sets are very different in determining a specific outcome. To show that, we don’t need to depict both the P-value and variance (mainly since these two values are very much correlated). Thus, I will depict only show one metric. I will stick with the P-value.

I will keep the category order the same between the two subgraphs. Doing so will create a “table lens” effect; it will show the individual values while demonstrating the lack of correlations between the two groups. Finally, I will convert the bars into points, primarily to reduce the data-ink ratio. Two additional arguments against bar charts, in this case, are the facts that the P-values of a statistical test cannot possibly be zero and that bar charts don’t allow log-scale, in case we’ll want to use it.

The result should look like this sketch.

Haifa on Friday. Street art, atmosphere, food.

How do you properly select a colormap for a graph? What makes the rainbow color map a wrong choice, and what are the proper alternatives?

Today, I stumbled upon a lengthy post that provides an in-depth review of the theory behind our color perception. The article concentrates on quantitative colormaps but also includes information relevant to selecting proper colors for categories.

https://nightingaledvs.com/color-in-a-perceptual-uniform-way/

If you never learned the theory behind the color and are interested in data visualization, I strongly suggest investing 45-60 minutes of your life in reading this post.

TL;DR War stories and pieces of advice from the high tech industry veteran.

I read this book following recomendations by Reem Sherman, the host of the excellent (!!!) podcast Geekonomy (in Hebrew).

Ben Horowitz is a veteran manager and entrepreneur who found the company Opsware, which Hewlett-Packard acquired in 2007. This book describes Horotwitz’s journey in Opsware from the foundation to the sale. Book’s second part is a collection of advice to working and aspiring CEOs. The last part is, actually, an advertisement for Horowitz’s new project – a VC company.

Things that I liked

The behind the scenes stories are interesting and inspiring.
Ben Horowitz devoted the second part of the book to share his experience as a CEO with other actual or aspiring CEOs. I don’t work as a CEO, nor do I see myself in that position in the future. However, this part is valuable for people like me because it provides insights into how CEOs think. Moreover, “The Hard Things” is a popular book, and many managers learn from it.

Things that I didn’t like.

Ben Horowitz was a manager during the early days of the high-tech industry. As such, parts of his attitude are outdated. The most prominent example for this problem is a story that Horowitz tells, in which he asked the entire company to work 12+ hours a day, seven days a week for several months. He was very proud about this, but IMO, employees will not accept such a request in today’s climate.

The bottom line: 4/5

Tishrei is the seventh month of the Hebrew calendar that starts with Rosh-HaShana — the Hebrew New Year. It is a 30 days month that usually occurs in September-October. One interesting feature of Tishrei is the fact that it is full of holidays: Rosh-HaShana (New Year), Yom Kippur (Day of Atonement), first and last days of Sukkot (Feast of Tabernacles) **. All these days are rest days in Israel. Every holiday eve is also a *de facto rest day in many industries (high tech included). So now we have 8 resting days that add to the usual Friday/Saturday pairs, resulting in very sparse work weeks. But that’s not all: the period between the first and the last Sukkot days are mostly considered as half working days. Also, the children are at home since all the schools and kindergartens are on vacation so we will treat those days as half working days in the following analysis.

I have counted the number of business days during this 31-day period (one day before the New Year plus the entire month of Tishrei) between for a perios of several years.

Overall, this period consists of between 14 to 17 working days in a single month (31 days, mind you). This year, we only have 14 working days during the Tishrei holiday period. This is how the working/not-working time during this month looks like:

Now, having some vacation is nice, but this month is absolutely crazy. There is not a single full working week during this month. It is very similar to the constantly interrupt work day, but at a different scale.

So, next time you wonder why your Israeli colleague, customer or partner barely works during September-October, recall this post.

(*) New Year starts in the seventh’s month? I know this is confusing. That’s because we number Nissan – the month of the Exodus from Egypt as the first month.

(**)If you are an observing Jew, you should add to this list Fast of Gedalia, but we will omit it from this discussion

Usually, I keep my blog for professional news only, but this time, I’ll make an exception.

This frame is from a video that was taken a couple of days ago, less than one hour away from my home. Note how many people are there.

Some people will claim that what we see is a peaceful protest by Palestinians against the Israeli occupation. Being a son and a grandson to the Holocaust survivors, I find it hard to connect to the peacefulness of what I see. I don’t have to hear them chanting “from the River to the Sea Palestine will be free” to understand that what they, and many thousands more, really mean is “free of Jews”.

Those who know me, know that I always care with me a cheep and thin notebook which I use as an extension to my mind. Today, I opened a new notebook, and this is a good opportunity to share some links about my productivity system.

• Start with the post “The best productivity system I know”
• Failed attempt with tangible boards is here. This approach has an interesting idea behind it, but I couldn’t stick with it. YMMW
• Failed attempt with digital/analog/tangible combo is here.

I stumbled upon a great graph that tells a complex story compellingly.

Comparison of two COVID-19 waves in the UK, taken from here.

This graph compares the last two waves of COVID-19 in the United Kingdom and is shows so clearly that the new wave (that is supposedly composed of the Delta variant) is much more infections on the one hand, but on the other hand, causes much less damage. Is the more moderate damage the result of the Delta variant nature of the protective effect of the vaccination is still an open question, but the difference is still striking.

To all my friends outside Israel. Do you want to know how the majority of Israelis see the shitty situation we are in? This short video does a good job summarizing it.

https://www.facebook.com/100462013796/videos/537389057252051?__cft__[0]=AZUvYpfaSRjJg_dVRoxwC7U7jmh-t2meeDW48n-IiYtS8d-PgX5o4WGqeConOtdTyC2DY_BagXlldPPzIE4PUgaoh1T_pSh_JCOIvo7BK1NbDifQEGvD07HxuO9pFuEtZeXFpCNSWfBiiZCtxcBbeG8l

My podcast is mostly in Hebrew, but this interview was recorded in English. I hope you will enjoy it

Martin Remy has been managing teams of data engineers and data scientists for more than a decade, and he has been doing so remotely. What lessons can we learn from Martin? לינקים חשובים: https://marting.blog https://martinremy.com עמוד הפייסבוק של ההסכת: https://www.facebook.com/reayonavodapodcast/ עמוד הבית שלי https://gorelik.net/about הרשמו להסכת ב־ גוגל פודקאסטס, ספוטיפיי, אפל מיוזיק, פודבין ובכל פלטפורמה […]

רעיון 38. Managing remotely — בוריס גורליק

This week, I mark an important milestone in my professional life. It is an excellent opportunity to start a new productivity notebook and tell you about the latest evolution of the best productivity system I know.

To sum up, I use a custom variant of Mark Forster’s Final Version productivity system that uses a plain notebook to track, prioritize, and eliminate tasks. Using a physical notebook, as opposed to an electronic tool, is a massive boost in productivity, as it forces you to process your priorities in an unplugged mode, without any distractions.

When I was a freelancer, I felt forced to use a combination of a physical book and an electronic system (http://todoist.com/), but that didn’t work too well for me, the connected nature of this (and any other) app kept distracting me. I also played with a combination of a notebook and a portable kanban board. That didn’t work out for me either. So, right now, I’m back to a physical notebook with a small addition.

I now have two notebooks. The first one is a small (80 pages) soft notebook that I use to track and prioritize tasks (as in Mark Forster’s system). I also use this notebook to reflect on what’s going on, write questions to my future self, and document my decisions.

The second, larger notebook is used for note keeping, drafts and sketches. The fact that the notebook is vertically bound allows me seemingly switching from Hebrew (that is written from right to left) and English. When a sketch of a draft isn’t relevant anymore, I tear the draft pages away; and I use a small binder to keep the note pages together for future reference.

Overall, I like this combo very much and it fits my workflow well.

In January 2020, I started a new experiment. I quit what was a dream job and became a freelancer. Today, the experiment is over. This post serves as omphaloskepsis - a short reflection on what went well and what could have worked better.

What worked well?

To sum up, I declare this experiment successful. I had a chance to work with several very interesting companies. I got exposed to business models of which I wasn’t aware. Most importantly, I met new intelligent and ambitious people. I also had a chance to feel by myself how it feels to be self-employed, to see the behind-the-scenes of several freelancers and entrepreneurs. I learned to appreciate the audacity and the courage of people who don’t rely on monthly paychecks and take much more responsibility for their lives than the vast majority of the “salarymen.”

Let’s talk about money. Was it worth it in terms of \(\)$ (or ₪₪₪₪₪₪)? Objectively speaking, my financial situation remained approximately unchanged. Towards the end of the experiment, I found myself overbooked, which means that, in theory, I could have increased my income substantially. But this is only in theory. In practice, I decided to end the freelance experiment and “settle down”.

What could have been better?

So, was it peachy? Not at all. For me, being a freelancer is much more stressful than being a hired employee. The stress does not come exclusively from the need to make sure one has enough projects in the pipeline (I had enough of them, most of the time). The more significant source of stress came from the lack of focus, the need for EXTREME context switching, and the lack of a team.

I did receive one suggestion to mitigate this source of stress; however, when I heard it, I already had several job offers and was already 90% committed to accepting the position at MyBiotics.

To sum up

I’m am very happy I did this experiment. I learned a lot; I enjoyed a lot (and suffered a lot too), I met new people, and I changed the way I think about many things. Was it a good idea? Yes, it was. Should you try becoming a freelancer? How the hell can I know that? It’s your life; you enjoy the success and take the risk of failure.

I’m excited to announce that I’m joining MyBiotics Pharma Ltd as the company’s Head of Data and Bioinformatics. I have been working with this fantastic company and its remarkable people as a freelancer for fourteen fruitful months. But today, I join the MyBiotics family as a full-time member. Together, we will strive to better understanding the interactions between humans and their microbiome to improve health and well-being.

rbt

Almost one year after it was originally published, I stumbled upon this powerful post.

Today, June 10th 2020, black academic scientists are holding a strike in solidarity with Black Lives Matter protests. I strike with them and for them. This is why: I began to understand the enormity of racism against blacks thirty five years ago when I was 12 years old. A single event, in which I witnessed […]

Black lives matter

The great Robert Kosara, also known as the “eager eyes” has started publishing a series of videos he calls Chart Appreciation. In these videos, Robert takes a piece of data visualization from a reputable and known source, and discusses why this particular piece is so good, what decisions were made that made it possible, what alternatives are, and more. If you consider yourself an intermediate or advanced practitioner of data visualization, you should subscribe. Here’s one example.

From time to time, people send me emails asking for career advice. Here’s one recent exchange.

Hi Boris,

I am currently trying to decide on a career move and would like to ask for your advice.

I have a MSc from a leading university in ML, without thesis.

I have 5 years of experience in data science at , producing ML based pipelines for the products. I have experience with Big Data (Spark, …), ML, deploying models to production…

However, I feel that I missed doing real ML complicated stuff. Most of the work I did was to build pipelines, training simple models, do some basic feature engineering… and it worked good enough.

Well, this IS the real ML job for 91.4%* of data scientists. You were lucky to work in a company with access to data and has teams dedicated to keeping data flowing, neat, and organized. You worked in a company with good work ethics, surrounded by smart people, and, I guess, the computational power was never a big issue. Most of the data scientists that I know don’t have all these perks. Some have to work alone; others need to solve “dull” engineering problems, find ways to process data on suboptimal computers or fight with a completely unstandardized data collection process. In fact, I know a young data scientist who quit their first post-Uni job after less than six months because she couldn’t handle most of these problems.

However I don’t have any real research experience. I never published any paper, and feel like I always did easy stuff. Therefore, I lack confidence in the ML domain. I feel like what I’ve been doing is not complicated and I could be easily replaced.

This is a super valid concern. I am surprised how few people in our field think about it. On the one hand, most ML practitioners don’t publish papers because they are busy doing the job they are paid for. I am a big proponent of teaching as a means of professional growth. So, you can decide to teach a course in a local meetup, local college, in your workplace, or at a conference. Teaching is an excellent way to improve your communication skills, which are the best means for job security (see this post).

Since you work at XXXXX , I suggest talking to your manager and/or HR representative. I’m SURE that they will have some ideas for a research project that you can take full-time or part-time to help you grow and help your business unit. This brings me to your next question.

I feel like having a research experience/doing a PhD may be an essential part to stay relevant in the long term in the domain. Also, having an expertise in one of NLP/Computer Vision may be very valuable.

I agree. Being a Ph.D. and an Israeli (we have one of the largest Ph.D. percentages globally) makes me biased.

I got 2 offers:

• One with , to do research in NLP and Computer Vision. [...] which is focused on doing research and publishing papers [...]

• One with a very fast growing insurance startup, for a data scientist position, as a part of the founding team team. […] However, I feel it would be the continuation of my current position as a data scientist, and I would maybe miss on this research component in my career.

You can explore a third option: A Ph.D. while working at your current place of work. I know for a fact that this company allows some of their employees to pursue a Ph.D. while working. The research may or may not be connected to their day job.

I am very hesitant because

• I am not sure focusing on ML models in a research team would be a good use of my time as ML may be commoditised, and general DS may be more future-proof. Also I am concerned about my impact there.

• I am not sure that I would have such a great impact in the DS team of the startup, due to regulations in the pricing model [of that company], and the fact that business problems may be solved by outsourced tools.

These are hard questions to answer. First of all, one may see legal constraints as a “feature, not a bug,” as they force more creative thinking and novel approaches. Many business problems may indeed be solved by outsourcing, but this usually doesn’t happen in problems central to the company’s success since these problems are unique enough to not fit an off-the-shelf product. You also need to consider your personal preferences because it is hard to be good at something you hate doing.

From time to time, I give career advice. When the question or the answer is general enough, I publish them in a post like this. You may read all of these posts here.

I invited Dr. Charles Earl for this episode of my podcast “Job Interview” to talk about racial discrimination at the workplace and fairness in machine learning.

Dr. Charles Earl is a data scientist in Automattic, my previous place of work. Charles holds a Ph.D. in computer science, M.A. in education, M.Sc in Electrical engineering, and B.Sc in mathematics. His career covered a position of assistant professor and a wide range of hands-on, managerial, and consulting roles in the field that we like to call today “data science.”

But there is another aspect in Dr. Earl. His skin is brown. He was born to an African-American family in Atlanta, GA, in the 1960s when racial segregation was explicitly legal. I am sure that this fact affected Charles’ entire life, personal and professional.

Links

• charlesearl.blog/
• Birth of Ghana
• W. E. B. Du Bois’s Data Portraits: Visualizing Black America
• How 20th Century Camera Film Captured a Snapshot of American Bias

If you know Hebrew, follow my podcast Job Interview (Reayon Avoda), and This Week in the Middle East

Deena Gergis is a data science lead at Bayer. I recently discovered Deena’s article on LinkedIn titled “Five Things I Wish I Knew About Real-Life AI.” I think that this article is a great piece of a career advice for all the current and aspiring data scientists, as well as for all the professionals who work with them. Let’ me take Deena’s headings and add my 2 cents.

One. It is all about the delivered value, not the method.

I fully agree with this one. Nobody cares whether you used a linear regression or recurrent neural network. Nobody really cares about p-values or r-squared. What people need are results, insights, or working products. Simple, right?

Two. Packaging does matter

Again, well said. The way you present your solution to your colleagues, customers, or stakeholders can determine whether your project will get more funds and resources or not.

Three. Doing the right things != doing things right.

Exactly. Citing Deena: “you might be perfectly predicting a KPI that no one cares about.” Enough said.

Four. Set realistic expectations.

Not everybody realizes that “machine learning” and “artificial intelligence” are not a synonym of “magic” but rather a form of statistics (I hope “real” statisticians won’t get mad at me here). The principle “garbage in - garbage out” holds in machine learning. Moreover, sometimes, ML systems amplify the garbage, resulting in “garbage in, tons of garbage out”.

Five. Keep humans in the loop.

Let me cite Deena again: “My customers are my partners, not just end-users.” Note that by “customers,” we don’t only mean walk-in clients, but also any internal customer, project manager, even a colleague who works on the same project. They are all partners with unique insights, domain knowledge, and experience. Use them to make your work better.

Read the original article here. Deena Gergis has several more articles on LinkedIn here. And if you know Arabic, you might want to watch Deena’s videos on YouTube here. Unfortunately, my Arabic is not good enough to understand her Egyptian accent, but I suspect that her videos are as good as her writings.

I started following data visualization news and opinions quite a few years ago. One of the first bloggers who were active in this area NeurDojo, by the (now) professor Zen Faulkes. On of Zen’s spin-off blogs was devoted to better posters. This poster blog is called, surprisingly enough, Better Posters. Since I’m not in academia anymore, stopped caring about posters many years ago. Today, I stumbled upon this blog and was pleasantly surprised to discover that Better Posters is still active and that it is also now a book.

Danny Lieberman managed teams of programmers before I couldn’t read, so when Danny writes a post as bold and blunt as this, you should read it.

Click the picture to go to the full text.

Oh, if you speak Hebrew, you should listen to Danny Lieberman talking in my podcast [here].

As data people, we need to work with files: we use files to save and load data, models, configurations, images, and other things. When possible, I prefer working with local files because it’s fast and straightforward. However, sometimes, the production code needs to work with data stored on S3. What do we do? Until recently, you would have to rewrite multiple parts of the code. But not anymore. I created a sshalosh package that solves so many problems and spares a lot of code rewriting. Here’s how you work with it:

if work_with_s3:
    s3_config = {
      "s3": {
        "defaultBucket": "bucket",
        "accessKey": "ABCDEFGHIJKLMNOP",
        "accessSecret": "/accessSecretThatOnlyYouKnow"
      }
    }
    
else:
    s3_config = None
serializer = sshalosh.Serializer(s3_config)

# Done! From now on, you only need to deal with the business logic, not the house-keeping

# Load data & model
data = serializer.load_json('data.json')
model = serializer.load_pickle('model.pkl')

# Update
data = update_with_new_examples()
model.fit(data)

# Save updated objects
serializer.dump_json(data, 'data.json')
serializer.dump_pickle(model, 'model.pkl')

As simple as that.
The package provides the following functions.

• path_exists
• rm
• rmtree
• ls
• load_pickle, dump_pickle
• load_json, dump_json

There is also a multipurpose open function that can open a file in read, write or append mode, and returns a handler to it.

How to install? How to contribute?

The installation is very simple: pip install sshalosh-borisgorelik
and you’re done. The code lives on GitHub under http://github.com/bgbg/shalosh. You are welcome to contribute code, documentation, and bug reports.

The name is strange, isn’t it?

Well, naming is hard. In Hebrew, “shalosh” means “three”, so “sshalosh” means s3. Don’t overanalyze this. The GitHub repo doesn’t have the extra s. My bad

TL;DR Very shallow and uninformative. It could be an OK series of blog posts for complete novices, but not a book.

The Persuasion Slide by Richard Dooley was a disappointment for me. I love Dooley’s podcast Brainfluence, and I was sure that Richard’s book would full of in-depth knowledge and case studies. However, it contained neither.

The only contribution of this book is the analogy between a sale process and an amusement part slide. The theory behind the book is mostly presented as a ground truth with almost no explanation or support from research. One will gain much more knowledge and understanding by reading Kahneman’s “Thinking, Fast and Slow,” Arieli’s “Predictably irrational.” or Weisman’s “59 seconds.”

Should I read this book?

No

I recently rediscovered a volcano plot – a scatter plot that aims to visualize changes in large populations.

Volcano plots are very technical and specialized and, most probably, are not a good fit for explanatory data visualization. However, they can be useful during the exploration phase, and they come with a set of well-established metrics.

Moreover, if you are lucky enough to have well-behaved data, the plots look very cool

From here

Of course, in real life, the data is messy. Add bad visualization practices to the mess and you get a marvel like this one

From here

The bottom line: if you have two populations to compare, consider volcano plots. But do remember dataviz good practices.